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This paper presents the implementation of a smart environment that employs Ambient Intelligence technologies in order to augment a typical hospital room with smart features that assist both patients and medical staff. In this environment various wireless and wired sensor technologies have been

This paper presents the implementation of a smart environment that employs Ambient Intelligence technologies in order to augment a typical hospital room with smart features that assist both patients and medical staff. In this environment various wireless and wired sensor technologies have been integrated, allowing the patient to control the environment and interact with the hospital facilities, while a clinically oriented interface allows for vital sign monitoring. The developed applications are presented both from a patient’s and a doctor’s perspective, offering different services depending on the user’s role. The results of the evaluation process illustrate the need for such a service, leading to important conclusions about the usefulness and crucial role of AmI in health care.
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This paper outlines an information system for tourists using collective public transport based on mobile devices with limited computation and wireless connection capacities. In this system, the mobile device collaborates with the vehicle infrastructure in order to provide the user with multimedia (visual

This paper outlines an information system for tourists using collective public transport based on mobile devices with limited computation and wireless connection capacities. In this system, the mobile device collaborates with the vehicle infrastructure in order to provide the user with multimedia (visual and audio) information about his/her trip. The information delivered, adapted to the user preferences, is synchronized with the passage of vehicles through points of interest along the route, for example: bus stops, tourist sights, public service centres, etc.
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Taking care of elders in a nursing home is not an easy task. Caregivers face two major problems: a lack of awareness of the situations surrounding the elderly care and the lack of information regarding the availability and the activities of other caregivers

Taking care of elders in a nursing home is not an easy task. Caregivers face two major problems: a lack of awareness of the situations surrounding the elderly care and the lack of information regarding the availability and the activities of other caregivers to support their coordination process. Various efforts have proposed solutions to cope with these problems, but they do it without considering all the requirements imposed by the criticality of this type of environment. In this paper we propose CANoE, a model for the design of context-aware notifications in critical environments, such as a nursing home. The main feature of this model is that it considers three sources of context (the environment, and the issuer and the receiver of the notification) for adapting the content, the terms of delivery and the presentation of the notification message. Based on the CANoE model we developed the CANoE-Aw and CU-IDA systems, which were evaluated through two case studies in a nursing home. The results of these evaluations provide evidence that caregivers achieved an increased awareness of the situations of care of the elderly and perceived the systems as adequate tools to support their coordination while attending a situation of care.
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The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an

The aim of the present work was to develop a method based on image analysis for describing soil detachment caused by the impact of a single water drop. The method consisted of recording tracks made by splashed particles on blotting paper under an optical microscope. The analysis facilitated division of the recorded particle tracks on the paper into drops, “comets” and single particles. Additionally, the following relationships were determined: (i) the distances of splash; (ii) the surface areas of splash tracks into relation to distance; (iii) the surface areas of the solid phase transported over a given distance; and (iv) the ratio of the solid phase to the splash track area in relation to distance. Furthermore, the proposed method allowed estimation of the weight of soil transported by a single water drop splash in relation to the distance of the water drop impact. It was concluded that the method of image analysis of splashed particles facilitated analysing the results at very low water drop energy and generated by single water drops.
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Intraoral devices for bite-force sensing have several applications in odontology and maxillofacial surgery, as bite-force measurements provide additional information to help understand the characteristics of bruxism disorders and can also be of help for the evaluation of post-surgical evolution and for comparison of

Intraoral devices for bite-force sensing have several applications in odontology and maxillofacial surgery, as bite-force measurements provide additional information to help understand the characteristics of bruxism disorders and can also be of help for the evaluation of post-surgical evolution and for comparison of alternative treatments. A new system for measuring human bite forces is proposed in this work. This system has future applications for the monitoring of bruxism events and as a complement for its conventional diagnosis. Bruxism is a pathology consisting of grinding or tight clenching of the upper and lower teeth, which leads to several problems such as lesions to the teeth, headaches, orofacial pain and important disorders of the temporomandibular joint. The prototype uses a magnetic field communication scheme similar to low-frequency radio frequency identification (RFID) technology (NFC). The reader generates a low-frequency magnetic field that is used as the information carrier and powers the sensor. The system is notable because it uses an intra-mouth passive sensor and an external interrogator, which remotely records and processes information regarding a patient’s dental activity. This permits a quantitative assessment of bite-force, without requiring intra-mouth batteries, and can provide supplementary information to polysomnographic recordings, current most adequate early diagnostic method, so as to initiate corrective actions before irreversible dental wear appears. In addition to describing the system’s operational principles and the manufacture of personalized prototypes, this report will also demonstrate the feasibility of the system and results from the first in vitro and in vivo trials.
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The electro-mechanical characteristics of piezoelectric actuators which have being driven are evaluated in this paper. The force generated by actuators is measured as an inertial force of a corner cub prism which is attached to the actuators. The Doppler frequency shift of a

The electro-mechanical characteristics of piezoelectric actuators which have being driven are evaluated in this paper. The force generated by actuators is measured as an inertial force of a corner cub prism which is attached to the actuators. The Doppler frequency shift of a laser beam, due to the motion of actuator, is accurately measured by a heterodyne interferometer. Subsequently, the mechanical quantities, such as velocity, acceleration, force, power and displacement, are calculated from the Doppler frequency shift. With the measurement results of current and voltage of the actuator, the relationships between electrical and mechanical characteristics are evaluated.
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One of the main challenges of today’s society is the need to fulfill at the same time the two sides of the dichotomy between the growing energy demand and the need to look after the environment. Smart Grids are one of the answers:

One of the main challenges of today’s society is the need to fulfill at the same time the two sides of the dichotomy between the growing energy demand and the need to look after the environment. Smart Grids are one of the answers: intelligent energy grids which retrieve data about the environment through extensive sensor networks and react accordingly to optimize resource consumption. In order to do this, the Smart Grids need to understand the existing relationship between energy demand and a set of relevant climatic variables. All smart “systems” (buildings, cities, homes, consumers, etc.) have the potential to employ their intelligence for self-adaptation to climate conditions. After introducing the Smart World, a global framework for the collaboration of these smart systems, this paper presents the relationship found at experimental level between a range of relevant weather variables and electric power demand patterns, presenting a case study using an agent-based system, and emphasizing the need to consider this relationship in certain Smart World (and specifically Smart Grid and microgrid) applications.
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A fully integrated humidity sensor chip was designed, implemented, and tested. Utilizing the micro-stamping technology, the pseudo-3D sensor system-on-chip (SSoC) architecture can be implemented by stacking sensing materials directly on the top of a CMOS-fabricated chip. The fabricated sensor system-on-chip (2.28 mm ×

A fully integrated humidity sensor chip was designed, implemented, and tested. Utilizing the micro-stamping technology, the pseudo-3D sensor system-on-chip (SSoC) architecture can be implemented by stacking sensing materials directly on the top of a CMOS-fabricated chip. The fabricated sensor system-on-chip (2.28 mm × 2.48 mm) integrated a humidity sensor, an interface circuit, a digital controller, and an On-Off Keying (OOK) wireless transceiver. With low power consumption, i.e., 750 μW without RF operation, the sensitivity of developed sensor chip was experimentally verified in the relative humidity (RH) range from 32% to 60%. The response time of the chip was also experimentally verified to be within 5 seconds from RH 36% to RH 64%. As a consequence, the implemented humidity SSoC paves the way toward the an ultra-small sensor system for various applications.
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The use of an exfoliated graphite (EG) electrode in the square wave voltammetric detection of bisphenol A (a model phenolic pollutant) in water, whereby the phenolic electrode fouling challenge is mitigated, is described. The oxidation peak of BPA was observed at about 0.45

The use of an exfoliated graphite (EG) electrode in the square wave voltammetric detection of bisphenol A (a model phenolic pollutant) in water, whereby the phenolic electrode fouling challenge is mitigated, is described. The oxidation peak of BPA was observed at about 0.45 V in phosphate buffer solution at pH 10. The current response exhibited a linear relationship with the concentration over a range from 1.56 µM–50 µM. The detection limit was calculated to be 0.76 µM. The EG electrode surface was renewed after each measurement with excellent reproducibility. A real sample application was also investigated.
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In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the

In both military and civilian applications, the inertial navigation system (INS) and the global positioning system (GPS) are two complementary technologies that can be integrated to provide reliable positioning and navigation information for land vehicles. The accuracy enhancement of INS sensors and the integration of INS with GPS are the subjects of widespread research. Wavelet de-noising of INS sensors has had limited success in removing the long-term (low-frequency) inertial sensor errors. The primary objective of this research is to develop a novel inertial sensor accuracy enhancement technique that can remove both short-term and long-term error components from inertial sensor measurements prior to INS mechanization and INS/GPS integration. A high resolution spectral analysis technique called the fast orthogonal search (FOS) algorithm is used to accurately model the low frequency range of the spectrum, which includes the vehicle motion dynamics and inertial sensor errors. FOS models the spectral components with the most energy first and uses an adaptive threshold to stop adding frequency terms when fitting a term does not reduce the mean squared error more than fitting white noise. The proposed method was developed, tested and validated through road test experiments involving both low-end tactical grade and low cost MEMS-based inertial systems. The results demonstrate that in most cases the position accuracy during GPS outages using FOS de-noised data is superior to the position accuracy using wavelet de-noising.
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A novel k-winners-take-all (k-WTA) competitive learning (CL) hardware architecture is presented for on-chip learning in this paper. The architecture is based on an efficient pipeline allowing k-WTA competition processes associated with different training vectors to be performed concurrently. The

A novel k-winners-take-all (k-WTA) competitive learning (CL) hardware architecture is presented for on-chip learning in this paper. The architecture is based on an efficient pipeline allowing k-WTA competition processes associated with different training vectors to be performed concurrently. The pipeline architecture employs a novel codeword swapping scheme so that neurons failing the competition for a training vector are immediately available for the competitions for the subsequent training vectors. The architecture is implemented by the field programmable gate array (FPGA). It is used as a hardware accelerator in a system on programmable chip (SOPC) for realtime on-chip learning. Experimental results show that the SOPC has significantly lower training time than that of other k-WTA CL counterparts operating with or without hardware support.
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A novel lateral flow immunoassay (LFIA) signal ampliﬁcation strategy for the detection of Cry1Ab based on ampliﬁcation via a polylysine (PL) chain and biotin-streptavidin system (BSAS) is described. In this system, multiple fluorescence dyes (FL) were directly coated on the surface of PL

A novel lateral flow immunoassay (LFIA) signal ampliﬁcation strategy for the detection of Cry1Ab based on ampliﬁcation via a polylysine (PL) chain and biotin-streptavidin system (BSAS) is described. In this system, multiple fluorescence dyes (FL) were directly coated on the surface of PL and conjugated with antibody via the BSAS for construction of novel signal amplification (FLPL-BSAS-mAb1) conjugates, in which FL, PL and BSAS were employed to improve the sensitivity of LFIA. Compared with conventional LFIA, the sensitivity of FLPL-BSAS-mAb1-based LFIA was increased by approximately 100-fold. Quantified linearity was achieved in the value range of 0–1,000 pg/mL. The limit of detection (LOD) was reached 10 pg/mL after optimization of reaction conditions. To our knowledge, this represents one of the most sensitive LFIA for Cry1Ab yet reported. Furthermore, the detection time for this method was about 10 min. Therefore, it should be an attractive alternative compared to conventional immunoassays in routine control for Cry1Ab.
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Analysis of foot movement is essential in the treatment and prevention of foot-related disorders. Measuring the in-shoe foot movement during everyday activities, such as sports, has the potential to become an important diagnostic tool in clinical practice. The current paper describes the development

Analysis of foot movement is essential in the treatment and prevention of foot-related disorders. Measuring the in-shoe foot movement during everyday activities, such as sports, has the potential to become an important diagnostic tool in clinical practice. The current paper describes the development of a thin, flexible and robust capacitive strain sensor for the in-shoe measurement of the navicular drop. The navicular drop is a well-recognized measure of foot movement. The position of the strain sensor on the foot was analyzed to determine the optimal points of attachment. The sensor was evaluated against a state-of-the-art video-based system that tracks reflective markers on the bare foot. Preliminary experimental results show that the developed strain sensor is able to measure navicular drop on the bare foot with an accuracy on par with the video-based system and with a high reproducibility. Temporal comparison of video-based, barefoot and in-shoe measurements indicate that the developed sensor measures the navicular drop accurately in shoes and can be used without any discomfort for the user.
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Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential

Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential applications, and investigates the impact of various critical parameters on its performance. BLE represents a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on parameters such as connInterval and connSlaveLatency. According to theoretical results, the lifetime of a BLE device powered by a coin cell battery ranges between 2.0 days and 14.1 years. The number of simultaneous slaves per master ranges between 2 and 5,917. The minimum latency for a master to obtain a sensor reading is 676 µs, although simulation results show that, under high bit error rate, average latency increases by up to three orders of magnitude. The paper provides experimental results that complement the theoretical and simulation findings, and indicates implementation constraints that may reduce BLE performance.
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Maintaining inter-actor connectivity is extremely crucial in mission-critical applications of Wireless Sensor and Actor Networks (WSANs), as actors have to quickly plan optimal coordinated responses to detected events. Failure of a critical actor partitions the inter-actor network into disjoint segments besides leaving a coverage hole, and thus hinders the network operation. This paper presents a Partitioning detection and Connectivity Restoration (PCR) algorithm to tolerate critical actor failure. As part of pre-failure planning, PCR determines critical/non-critical actors based on localized information and designates each critical node with an appropriate backup (preferably non-critical). The pre-designated backup detects the failure of its primary actor and initiates a post-failure recovery process that may involve coordinated multi-actor relocation. To prove the correctness, we construct a formal specification of PCR using Z notation. We model WSAN topology as a dynamic graph and transform PCR to corresponding formal specification using Z notation. Formal specification is analyzed and validated using the Z Eves tool. Moreover, we simulate the specification to quantitatively analyze the efficiency of PCR. Simulation results confirm the effectiveness of PCR and the results shown that it outperforms contemporary schemes found in the literature.Full article

A microfibre device integrating a microfibre knot resonator in a Sagnac loop reflector is proposed for refractive index and temperature sensing. The reflective configuration of this optical structure offers the advantages of simple fabrication and ease of sensing. To achieve a balance between

A microfibre device integrating a microfibre knot resonator in a Sagnac loop reflector is proposed for refractive index and temperature sensing. The reflective configuration of this optical structure offers the advantages of simple fabrication and ease of sensing. To achieve a balance between responsiveness and robustness, the entire microfibre structure is embedded in low index Teflon, except for the 0.5–2 mm diameter microfibre knot resonator sensing region. The proposed sensor has exhibited a linear spectral response with temperature and refractive index. A small change in free spectral range is observed when the microfibre device experiences a large refractive index change in the surrounding medium. The change is found to be in agreement with calculated results based on dispersion relationships.
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In this paper compact alignment and position sensors based on coplanar waveguide (CPW) transmission lines loaded with split ring resonators (SRRs) are proposed. The structure consists of a folded CPW loaded with two SRRs tuned at different frequencies to detect both the lack

In this paper compact alignment and position sensors based on coplanar waveguide (CPW) transmission lines loaded with split ring resonators (SRRs) are proposed. The structure consists of a folded CPW loaded with two SRRs tuned at different frequencies to detect both the lack of alignment and the two-dimensional linear displacement magnitude. Two additional resonators (also tuned at different frequencies) are used to detect the displacement direction. The working principle for this type of sensor is explained in detail, and a prototype device to illustrate the potential of the approach has been designed and fabricated.
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Compared to the error factors of the Linear Array Digital Sun Sensor (DSS), those of the Area Array DSS are complicated and methods used for error compensation are not valid or simple enough. This paper presents the main error factors of the Area

Compared to the error factors of the Linear Array Digital Sun Sensor (DSS), those of the Area Array DSS are complicated and methods used for error compensation are not valid or simple enough. This paper presents the main error factors of the Area Array DSS and proposes an effective method to compensate them. The procedure of error compensation of Area Array DSS includes three steps. First, the geometric error of calibration is compensated; second, the coordinate map method is used to compensate the error caused by optical refraction; third, the high order polynomial-fitting method is applied to calculate the tangent of the sun angles; finally, the arc tangent method is used to calculate the sun angles. Experimental results of the product of the High Accuracy Sun Sensor indicate that the precision is better than 0.02° during the cone field of view (CFOV) of 10°, and the precision is better than 0.14° during the CFOV 10° to 64°. The proposed compensation method effectively compensates the major error factors and significantlyimproves the measure precision of the Area APS DSS.
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There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.
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Stationary range laser sensors for intruder monitoring, restricted space violation detections and workspace determination are extensively used in risky environments. In this work we present a linear based approach for predicting the presence of moving agents before they trespass a laser-based restricted space.

Stationary range laser sensors for intruder monitoring, restricted space violation detections and workspace determination are extensively used in risky environments. In this work we present a linear based approach for predicting the presence of moving agents before they trespass a laser-based restricted space. Our approach is based on the Taylor’s series expansion of the detected objects’ movements. The latter makes our proposal suitable for embedded applications. In the experimental results (carried out in different scenarios) presented herein, our proposal shows 100% of effectiveness in predicting trespassing situations.Several implementation results and statistics analysis showing the performance of our proposal are included in this work.
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In the Internet of Things (IoT) research arena, many efforts are devoted to adapt the existing IP standards to emerging IoT nodes. This is the direction followed by three Internet Engineering Task Force (IETF) Working Groups, which paved the way for research on

In the Internet of Things (IoT) research arena, many efforts are devoted to adapt the existing IP standards to emerging IoT nodes. This is the direction followed by three Internet Engineering Task Force (IETF) Working Groups, which paved the way for research on IP-based constrained networks. Through a simplification of the whole TCP/IP stack, resource constrained nodes become direct interlocutors of application level entities in every point of the network. In this paper we analyze some side effects of this solution, when in the presence of large amounts of data to transmit. In particular, we conduct a performance analysis of the Constrained Application Protocol (CoAP), a widely accepted web transfer protocol for the Internet of Things, and propose a service management enhancement that improves the exploitation of the network and node resources. This is specifically thought for constrained nodes in the abovementioned conditions and proves to be able to significantly improve the node energetic performance when in the presence of large resource representations (hence, large data transmissions).
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In this paper we propose an approach for the estimation of the slope of the walking surface during normal walking using a body-worn sensor composed of a biaxial accelerometer and a uniaxial gyroscope attached to the shank. It builds upon a state of

In this paper we propose an approach for the estimation of the slope of the walking surface during normal walking using a body-worn sensor composed of a biaxial accelerometer and a uniaxial gyroscope attached to the shank. It builds upon a state of the art technique that was successfully used to estimate the walking velocity from walking stride data, but did not work when used to estimate the slope of the walking surface. As claimed by the authors, the reason was that it did not take into account the actual inclination of the shank of the stance leg at the beginning of the stride (mid stance). In this paper, inspired by the biomechanical characteristics of human walking, we propose to solve this issue by using the accelerometer as a tilt sensor, assuming that at mid stance it is only measuring the gravity acceleration. Results from a set of experiments involving several users walking at different inclinations on a treadmill confirm the feasibility of our approach. A statistical analysis of slope estimations shows in first instance that the technique is capable of distinguishing the different slopes of the walking surface for every subject. It reports a global RMS error (per-unit difference between actual and estimated inclination of the walking surface for each stride identified in the experiments) of 0.05 and this can be reduced to 0.03 with subject-specific calibration and post processing procedures by means of averaging techniques.
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In this communication we discuss the development of ionophore based nanosensors for the detection and monitoring of histamine levels in vivo. This approach is based on the use of an amine-reactive, broad spectrum ionophore which is capable of recognizing and binding to

In this communication we discuss the development of ionophore based nanosensors for the detection and monitoring of histamine levels in vivo. This approach is based on the use of an amine-reactive, broad spectrum ionophore which is capable of recognizing and binding to histamine. We pair this ionophore with our already established nanosensor platform, and demonstrate in vitro and in vivo monitoring of histamine levels. This approach enables capturing rapid kinetics of histamine after injection, which are more difficult to measure with standard approaches such as blood sampling, especially on small research models. The coupling together of in vivo nanosensors with ionophores such as nonactin provide a way to generate nanosensors for novel targets without the difficult process of designing and synthesizing novel ionophores.
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Baseball and softball pitch types are distinguished by the path and speed of the ball which, in turn, are determined by the angular velocity of the ball and the velocity of the ball center at the instant of release from the pitcher’s hand.

Baseball and softball pitch types are distinguished by the path and speed of the ball which, in turn, are determined by the angular velocity of the ball and the velocity of the ball center at the instant of release from the pitcher’s hand. While radar guns and video-based motion capture (mocap) resolve ball speed, they provide little information about how the angular velocity of the ball and the velocity of the ball center develop and change during the throwing motion. Moreover, mocap requires measurements in a controlled lab environment and by a skilled technician. This study addresses these shortcomings by introducing a highly miniaturized, wireless inertial measurement unit (IMU) that is embedded in both baseballs and softballs. The resulting “ball-embedded” sensor resolves ball dynamics right on the field of play. Experimental results from ten pitches, five thrown by one softball pitcher and five by one baseball pitcher, demonstrate that this sensor technology can deduce the magnitude and direction of the ball’s velocity at release to within 4.6% of measurements made using standard mocap. Moreover, the IMU directly measures the angular velocity of the ball, which further enables the analysis of different pitch types.
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Systems of planar electrodes arranged on dielectric or piezoelectric layers are applied in numerous sensors and transducers. In this paper electrostatics of such electrode systems is presented and exploited in the analysis of distributed piezoelectric transducer dedicated to surface elastometry of biological tissues

Systems of planar electrodes arranged on dielectric or piezoelectric layers are applied in numerous sensors and transducers. In this paper electrostatics of such electrode systems is presented and exploited in the analysis of distributed piezoelectric transducer dedicated to surface elastometry of biological tissues characterized by large Poisson modulus. The fundamental Matlab® code for analyzing complex planar multiperiodic electrode systems is also presented.
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The aim of this work is to show the possibility of using fiber optic sensors to instrument inside parts of an artificial maxilla and measure internal tension transmitted by the orthodontic and orthopedic appliances to the dentition and the adjacent bone. Bragg gratings

The aim of this work is to show the possibility of using fiber optic sensors to instrument inside parts of an artificial maxilla and measure internal tension transmitted by the orthodontic and orthopedic appliances to the dentition and the adjacent bone. Bragg gratings written in a standard optical fiber were used to monitor the maxillary teeth and a multiplexed fiber was used to monitor the surface of the maxillary bone, transversally to the longest axis of the teeth. A Universal Test Machine was used to evaluate the sensitivity of the sensor to the vertical and lateral forces applied on the teeth. A wavelength shift of approximately 0.30 nm was detected when applying loads ranging from 0 to 20 N. By applying forces using the standard orthodontic appliances installed on the dentition it was possible to detect a range of forces between 0.025 N to 0.035 N during the activation of the arch wire and extra-oral forces. The use of the internal sensors in an artificial model made possible the monitoring of the resulting forces on the internal parts of the teeth and at the position where the strain takes place within the maxilla. The sensors detected that the orthodontic forces were not transmitted to the surface of the maxilla. This information is important to elucidate and to correlate undesirable effects as tooth root absorption and local pain during the orthodontic treatment.
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Finding out the optimal sampling positions for measurement of ventilation rates in a naturally ventilated building using tracer gas is a challenge. Affected by the wind and the opening status, the representative positions inside the building may change dynamically at any time. An

Finding out the optimal sampling positions for measurement of ventilation rates in a naturally ventilated building using tracer gas is a challenge. Affected by the wind and the opening status, the representative positions inside the building may change dynamically at any time. An optimization procedure using the Response Surface Methodology (RSM) was conducted. In this method, the concentration field inside the building was estimated by a three-order RSM polynomial model. The experimental sampling positions to develop the model were chosen from the cross-section area of a pitched-roof building. The Optimal Design method which can decrease the bias of the model was adopted to select these sampling positions. Experiments with a scale model building were conducted in a wind tunnel to achieve observed values of those positions. Finally, the models in different cases of opening states and wind conditions were established and the optimum sampling position was obtained with a desirability level up to 92% inside the model building. The optimization was further confirmed by another round of experiments.
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Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one

Induction motors fed through variable speed drives (VSD) are widely used in different industrial processes. Nowadays, the industry demands the integration of smart sensors to improve the fault detection in order to reduce cost, maintenance and power consumption. Induction motors can develop one or more faults at the same time that can be produce severe damages. The combined fault identification in induction motors is a demanding task, but it has been rarely considered in spite of being a common situation, because it is difficult to identify two or more faults simultaneously. This work presents a smart sensor for online detection of simple and multiple-combined faults in induction motors fed through a VSD in a wide frequency range covering low frequencies from 3 Hz and high frequencies up to 60 Hz based on a primary sensor being a commercially available current clamp or a hall-effect sensor. The proposed smart sensor implements a methodology based on the fast Fourier transform (FFT), RMS calculation and artificial neural networks (ANN), which are processed online using digital hardware signal processing based on field programmable gate array (FPGA).
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In this study, zinc oxide (ZnO) was a very good candidate for improving the sensitivity of gas sensor technology. The preparation of an electrospun ZnO nanostructured thin film on a 433 MHz Rayleigh wave based Surface Acoustic Wave (SAW) sensor and the investigation

In this study, zinc oxide (ZnO) was a very good candidate for improving the sensitivity of gas sensor technology. The preparation of an electrospun ZnO nanostructured thin film on a 433 MHz Rayleigh wave based Surface Acoustic Wave (SAW) sensor and the investigation of the acoustoelectric effect on the responses of the SAW sensor are reported. We prepared an electrospun ZnO nanostructured thin film on the SAW devices by using an electrospray technique. To investigate the dependency of the sensor response on the structure and the number of the ZnO nanoparticles, SAW sensors were prepared with different coating loads. The coating frequency shifts were adjusted to fall between 100 kHz and 2.4 MHz. The sensor measurements were performed against VOCs such as acetone, trichloroethylene, chloroform, ethanol, n-propanol and methanol vapor. The sensor responses of n-propanol have opposite characteristics to the other VOCs, and we attributed these characteristics to the elastic effect/acoustoelectric effect.
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A kind of surface plasmon resonance sensor based on grapefruit photonic crystal fiber (PCF) filled with different numbers of silver nanowires has been studied in this paper. The surface plasmon resonance modes and the sensing properties are investigated comprehensively using the finite element

A kind of surface plasmon resonance sensor based on grapefruit photonic crystal fiber (PCF) filled with different numbers of silver nanowires has been studied in this paper. The surface plasmon resonance modes and the sensing properties are investigated comprehensively using the finite element method (FEM). The simulation results show that the intensity sensitivity is related to nanowire numbers and the distance between two nanowires. The optimum value obtained is 2,400 nm/RIU, corresponding to a resolution of 4.51 × 10−5 RIU with a maximum distance of 2 μm. To a certain extent, the PCF filled with more nanowires is better than with just one. Furthermore, the air holes of grapefruit PCF are large enough to operate in practice. Moreover, the irregularity of the filled nanowires has no effect on sensitivity, which will be very convenient for the implementation of experiments.
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Although smartphone applications represent the most typical data consumer tool from the citizen perspective in environmental applications, they can also be used for in-situ data collection and production in varied scenarios, such as geological sciences and biodiversity. The use of standard protocols, such

Although smartphone applications represent the most typical data consumer tool from the citizen perspective in environmental applications, they can also be used for in-situ data collection and production in varied scenarios, such as geological sciences and biodiversity. The use of standard protocols, such as SWE, to exchange information between smartphones and sensor infrastructures brings benefits such as interoperability and scalability, but their reliance on XML is a potential problem when large volumes of data are transferred, due to limited bandwidth and processing capabilities on mobile phones. In this article we present a performance analysis about the use of SWE standards in smartphone applications to consume and produce environmental sensor data, analysing to what extent the performance problems related to XML can be alleviated by using alternative uncompressed and compressed formats.
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This paper introduces a revolutionary way to interrogate optical fiber sensors based on fiber Bragg gratings (FBGs) and to integrate the necessary driving optoelectronic components with the sensor elements. Low-cost optoelectronic chips are used to interrogate the optical fibers, creating a portable dynamic

This paper introduces a revolutionary way to interrogate optical fiber sensors based on fiber Bragg gratings (FBGs) and to integrate the necessary driving optoelectronic components with the sensor elements. Low-cost optoelectronic chips are used to interrogate the optical fibers, creating a portable dynamic sensing system as an alternative for the traditionally bulky and expensive fiber sensor interrogation units. The possibility to embed these laser and detector chips is demonstrated resulting in an ultra thin flexible optoelectronic package of only 40 μm, provided with an integrated planar fiber pigtail. The result is a fully embedded flexible sensing system with a thickness of only 1 mm, based on a single Vertical-Cavity Surface-Emitting Laser (VCSEL), fiber sensor and photodetector chip. Temperature, strain and electrodynamic shaking tests have been performed on our system, not limited to static read-out measurements but dynamically reconstructing full spectral information datasets.
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The strain sensitivity of a two-mode birefringent side-hole fiber is measured in the spectral domain. In a simple experimental setup comprising a broadband source, a polarizer, a two-mode birefringent side-hole fiber under varied elongations, an analyzer and a compact spectrometer, the spectral interferograms

The strain sensitivity of a two-mode birefringent side-hole fiber is measured in the spectral domain. In a simple experimental setup comprising a broadband source, a polarizer, a two-mode birefringent side-hole fiber under varied elongations, an analyzer and a compact spectrometer, the spectral interferograms are resolved. These are characterized by the equalization wavelength at which spectral interference fringes have the highest visibility (the largest period) due to the zero group optical path difference between the fundamental, the LP01 mode and the higher-order, the LP11 mode. The spectral interferograms with the equalization wavelength are processed to retrieve the phase as a function of the wavelength. From the retrieved phase functions corresponding to different elongations of a two-mode birefringent side-hole fiber under test, the spectral strain sensitivity is obtained. Using this approach, the intermodal spectral strain sensitivity was measured for both x and y polarizations. Moreover, the spectral polarimetric sensitivity to strain was measured for the fundamental mode when a birefringent delay line was used in tandem with the fiber. Its spectral dependence was also compared with that obtained from a shift of the spectral interferograms not including the equalization wavelength, and good agreement was confirmed.
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An optical sensor based on the coupling between the plasmonic and photonic resonance modes in metallic photonic crystals is investigated. Large-area metallic photonic crystals consisting of periodically arranged gold nanostructures with dimensions down to sub-100 nm are fabricated using solution-processible gold nanoparticles in

An optical sensor based on the coupling between the plasmonic and photonic resonance modes in metallic photonic crystals is investigated. Large-area metallic photonic crystals consisting of periodically arranged gold nanostructures with dimensions down to sub-100 nm are fabricated using solution-processible gold nanoparticles in combination with interference lithography or interference ablation, which introduces a variety of fabrication techniques for the construction of this kind of sensor device. Sensitivity of the plasmonic response of the gold nanostructures to the changes in the environmental refractive index is enhanced through the coupling between the narrow-band photonic resonance mode and the relatively broad-band plasmon resonance, which is recognized as a Fano-like effect and is utilized to explore sensors. Theoretical modeling shows the characterization and the optimization of the sensitivity of this kind of sensor device. Theoretical and experimental results are demonstrated for the approaches to improve the sensitivity of the sensor device.
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This paper introduces different data fusion methods which are used for an electrochemical measurement using a sensor array. In this study, we used ruthenium dioxide sensing membrane pH electrodes to form a sensor array. The sensor array was used for detecting the pH

This paper introduces different data fusion methods which are used for an electrochemical measurement using a sensor array. In this study, we used ruthenium dioxide sensing membrane pH electrodes to form a sensor array. The sensor array was used for detecting the pH values of grape wine, generic cola drink and bottled base water. The measured pH data were used for data fusion methods to increase the reliability of the measured results, and we also compared the fusion results with other different data fusion methods.
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Wireless sensor networks are becoming increasingly more common as a means to sense, measure, record and transmit data for scientific and engineering evaluation, remotely and autonomously. Usually, remotely located sensor nodes are powered by batteries which are recharged by solar or wind energy

Wireless sensor networks are becoming increasingly more common as a means to sense, measure, record and transmit data for scientific and engineering evaluation, remotely and autonomously. Usually, remotely located sensor nodes are powered by batteries which are recharged by solar or wind energy harvesters. Sometimes nodes are located in areas where these forms of energy harvesting are not possible due to local conditions, such as under the canopy of a forest. This article outlines the design and testing of a device capable of harvesting energy from tree movement, and shows the device powering a wireless sensor node continuously. The device uses the force and displacement of the movement of a tree trunk (of a 6 m tall tree) to drive an electromagnetic generator that recharges a nickel metal hydride battery. The battery stores the energy from which a ~0.5 mW wireless sensor node is powered continuously. This demonstrated method of energy harvesting may allow the placement and powering of nodes in locations previously not possible.
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Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although

Recent advances in technologies for capturing video data have opened a vast amount of new application areas in visual sensor networks. Among them, the incorporation of light wave cameras on Ambient Intelligence (AmI) environments provides more accurate tracking capabilities for activity recognition. Although the performance of tracking algorithms has quickly improved, symbolic models used to represent the resulting knowledge have not yet been adapted to smart environments. This lack of representation does not allow to take advantage of the semantic quality of the information provided by new sensors. This paper advocates for the introduction of a part-based representational level in cognitive-based systems in order to accurately represent the novel sensors’ knowledge. The paper also reviews the theoretical and practical issues in part-whole relationships proposing a specific taxonomy for computer vision approaches. General part-based patterns for human body and transitive part-based representation and inference are incorporated to an ontology-based previous framework to enhance scene interpretation in the area of video-based AmI. The advantages and new features of the model are demonstrated in a Social Signal Processing (SSP) application for the elaboration of live market researches.
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Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on

Traffic accidents are still one of the main health problems in the World. A number of measures have been applied in order to reduce the number of injuries and fatalities in roads, i.e., implementation of Advanced Driver Assistance Systems (ADAS) based on image processing. In this paper, a real time speed supervisor based on road sign recognition that can work both in urban and non-urban environments is presented. The system is able to recognize 135 road signs, belonging to the danger, yield, prohibition obligation and indication types, and sends warning messages to the driver upon the combination of two pieces of information: the current speed of the car and the road sign symbol. The core of this paper is the comparison between the two main methods which have been traditionally used for detection and recognition of road signs: template matching (TM) and neural networks (NN). The advantages and disadvantages of the two approaches will be shown and commented. Additionally we will show how the use of well-known algorithms to avoid illumination issues reduces the amount of images needed to train a neural network.
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GMR sensors are widely used in many industrial segments such as information technology, automotive, automation and production, and safety applications. Each area requires an adaption of the sensor arrangement in terms of size adaption and alignment with respect to the field source involved.

GMR sensors are widely used in many industrial segments such as information technology, automotive, automation and production, and safety applications. Each area requires an adaption of the sensor arrangement in terms of size adaption and alignment with respect to the field source involved. This paper deals with an analysis of geometric sensor parameters and the arrangement of GMR sensors providing a design roadmap for non-destructive testing (NDT) applications. For this purpose we use an analytical model simulating the magnetic flux leakage (MFL) distribution of surface breaking defects and investigate the flux leakage signal as a function of various sensor parameters. Our calculations show both the influence of sensor length and height and that when detecting the magnetic flux leakage of µm sized defects a gradiometer base line of 250 µm leads to a signal strength loss of less than 10% in comparison with a magnetometer response. To validate the simulation results we finally performed measurements with a GMR magnetometer sensor on a test plate with artificial µm-range cracks. The differences between simulation and measurement are below 6%. We report on the routes for a GMR gradiometer design as a basis for the fabrication of NDT-adapted sensor arrays. The results are also helpful for the use of GMR in other application when it comes to measure positions, lengths, angles or electrical currents.
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The present paper presents the design and development results of a system setup for measuring Young’s modulus of thin films by laser-induced surface acoustic waves based on the integration of two detection methods, namely, piezoelectric transducer detection and differential confocal detection, which may

The present paper presents the design and development results of a system setup for measuring Young’s modulus of thin films by laser-induced surface acoustic waves based on the integration of two detection methods, namely, piezoelectric transducer detection and differential confocal detection, which may be used for conducting consecutive or simultaneous measurements. After demonstrating the capabilities of each detection approach, it is shown how, depending on a wider range of applications, sample materials and measurement environments, the developed integrated system inherits and harnesses the main characteristics of its detection channels, resulting in an more practical and flexible equipment for determining Young’s modulus than traditional nanoindentation equipment, and also suitable for cross-validation purposes.
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Nitrogen compounds like urea and melamine are known to be commonly used for milk adulteration resulting in undesired intoxication; a well-known example is the Chinese episode occurred in 2008. The development of a rapid, reliable and economic test is of relevance in order

Nitrogen compounds like urea and melamine are known to be commonly used for milk adulteration resulting in undesired intoxication; a well-known example is the Chinese episode occurred in 2008. The development of a rapid, reliable and economic test is of relevance in order to improve adulterated milk identification. Cyclic voltammetry studies using an Au working electrode were performed on adulterated and non-adulterated milk samples from different independent manufacturers. Voltammetric data and their first derivative were subjected to functional principal component analysis (f-PCA) and correctly classified by the KNN classifier. The adulterated and non-adulterated milk samples showed significant differences. Best results of prediction were obtained with first derivative data. Detection limits in milk samples adulterated with 1% of its total nitrogen derived from melamine or urea were as low as 85.0 mg·L−1 and121.4 mg·L−1, respectively. We present this method as a fast and robust screening method for milk adulteration analysis and prevention of food intoxication.
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In this paper, we report our findings on implementing a synthetic phased array surface acoustic wave sensor to quantify bolt tension. Maintaining proper bolt tension is important in many fields such as for ensuring safe operation of civil infrastructures. Significant advantages of this

In this paper, we report our findings on implementing a synthetic phased array surface acoustic wave sensor to quantify bolt tension. Maintaining proper bolt tension is important in many fields such as for ensuring safe operation of civil infrastructures. Significant advantages of this relatively simple methodology is its capability to assess bolt tension without any contact with the bolt, thus enabling measurement at inaccessible locations, multiple bolt measurement capability at a time, not requiring data collection during the installation and no calibration requirements. We performed detailed experiments on a custom-built flexible bench-top experimental setup consisting of 1018 steel plate of 12.7 mm (½ in) thickness, a 6.4 mm (¼ in) grade 8 bolt and a stainless steel washer with 19 mm (¾ in) of external diameter. Our results indicate that this method is not only capable of clearly distinguishing properly bolted joints from loosened joints but also capable of quantifying how loose the bolt actually is. We also conducted detailed signal-to-noise (SNR) analysis and showed that the SNR value for the entire bolt tension range was sufficient for image reconstruction.
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In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every

In this paper, a pixel-based background modeling method, which uses nonparametric kernel density estimation, is proposed. To reduce the burden of image storage, we modify the original KDE method by using the first frame to initialize it and update it subsequently at every frame by controlling the learning rate according to the situations. We apply an adaptive threshold method based on image changes to effectively subtract the dynamic backgrounds. The devised scheme allows the proposed method to automatically adapt to various environments and effectively extract the foreground. The method presented here exhibits good performance and is suitable for dynamic background environments. The algorithm is tested on various video sequences and compared with other state-of-the-art background subtraction methods so as to verify its performance.
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In recent years, the number of proposed fall-detection systems that have been developed has increased dramatically. A threshold-based algorithm utilizing an accelerometer has been used to detect low-complexity falling activities. In this study, we defined activities in which the body’s center of gravity

In recent years, the number of proposed fall-detection systems that have been developed has increased dramatically. A threshold-based algorithm utilizing an accelerometer has been used to detect low-complexity falling activities. In this study, we defined activities in which the body’s center of gravity quickly declines as falling activities of daily life (ADLs). In the non-falling ADLs, we also focused on the body’s center of gravity. A hyperplane of the support vector machine (SVM) was used as the separating plane to replace the traditional threshold method for the detection of falling ADLs. The scripted and continuous unscripted activities were performed by two groups of young volunteers (20 subjects) and one group of elderly volunteers (five subjects). The results showed that the four parameters of the input vector had the best accuracy with 99.1% and 98.4% in the training and testing, respectively. For the continuous unscripted test of one hour, there were two and one false positive events among young volunteers and elderly volunteers, respectively.
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Changes in mass loading on the surface of acoustic biosensors result in output frequency shifts which provide precise measurements of analytes. Therefore, to detect a particular biomarker, the sensor delay path must be judiciously designed to maximize sensitivity and specificity. B-cell lymphoma 2

Changes in mass loading on the surface of acoustic biosensors result in output frequency shifts which provide precise measurements of analytes. Therefore, to detect a particular biomarker, the sensor delay path must be judiciously designed to maximize sensitivity and specificity. B-cell lymphoma 2 protein (Bcl-2) found in urine is under investigation as a biomarker for non-invasive early detection of ovarian cancer. In this study, surface chemistry and biofunctionalization approaches were evaluated for their effectiveness in presenting antibodies for Bcl-2 capture while minimizing non-specific protein adsorption. The optimal combination of sequentially adsorbing protein A/G, anti-Bcl-2 IgG and Pluronic F127 onto a hydrophobic surface provided the greatest signal-to-noise ratio and enabled the reliable detection of Bcl-2 concentrations below that previously identified for early stage ovarian cancer as characterized by a modified ELISA method. Finally, the optimal surface modification was applied to a prototype acoustic device and the frequency shift for a range of Bcl-2 concentration was quantified to demonstrate the effectiveness in surface acoustic wave (SAW)-based detection applications. The surface functionalization approaches demonstrated here to specifically and sensitively detect Bcl-2 in a working ultrasonic MEMS biosensor prototype can easily be modified to detect additional biomarkers and enhance other acoustic biosensors.
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A novel and promising method to measure low levels of NOx utilizes the accumulating sensor principle. During an integration cycle, incoming NOx molecules are stored in a sensitive layer based on an automotive lean NOx trap (LNT) material that changes

A novel and promising method to measure low levels of NOx utilizes the accumulating sensor principle. During an integration cycle, incoming NOx molecules are stored in a sensitive layer based on an automotive lean NOx trap (LNT) material that changes its electrical resistivity proportional to the amount of stored NOx, making the sensor suitable for long-term detection of low levels of NOx. In this study, the influence of the thickness of the sensitive layer, prepared by multiple screen-printing, is investigated. All samples show good accumulating sensing properties for both NO and NO2. In accordance to a simplified model, the base resistance of the sensitive layer and the sensitivity to NOx decrease with increasing thickness. Contrarily, the sensor response time increases. The linear measurement range of all samples ends at a sensor response of about 30% resulting in an increase of the linearly detectable amount with the thickness. Hence, the variation of the thickness of the sensitive layer is a powerful tool to adapt the linear measurement range (proportional to the thickness) as well as the sensitivity (proportional to the inverse thickness) to the application requirements. Calculations combining the sensor model with the measurement results indicate that for operation in the linear range, about 3% of the LNT material is converted to nitrate.
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Hydrogen is a valuable fuel for the next energy scenario. Unfortunately, hydrogen is highly flammable at concentrations higher than 4% in air. This aspect makes the monitoring of H2 leaks an essential issue for safety reasons, especially in the transportation field. In

Hydrogen is a valuable fuel for the next energy scenario. Unfortunately, hydrogen is highly flammable at concentrations higher than 4% in air. This aspect makes the monitoring of H2 leaks an essential issue for safety reasons, especially in the transportation field. In this paper, nanocomposites based on Pt-doped TiO2/multiwalled carbon nanotubes (MWCNTs) have been introduced as sensitive materials for H2 at low temperatures. Pt-TiO2/MWNTs nanocomposites with different composition have been prepared by a simple wet chemical procedure and their morphological, microstructural and electrical properties were investigated. Resistive thick-film devices have been fabricated printing the hybrid nanocomposites on alumina substrates provided with Pt interdigitated electrodes. Electrical tests in air have shown that embedding MWCNTs in the TiO2 matrix modify markedly the electrical conductivity, providing a means to decrease the resistance of the sensing layer. Pt acts as a catalytic additive. Pt-TiO2/MWNTs-based sensors were found to be sensitive to hydrogen at concentrations between 0.5 and 3% in air, satisfying the requisites for practical applications in hydrogen leak detection devices.
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Fiber-optic interferometric sensors based on graded-index multimode fibers have very high refractive-index sensitivity, as we previously demonstrated. In this paper, spatial-frequency multiplexing of this type of fiber-optic refractive index sensors is investigated. It is estimated that multiplexing of more than 10 such sensors

Fiber-optic interferometric sensors based on graded-index multimode fibers have very high refractive-index sensitivity, as we previously demonstrated. In this paper, spatial-frequency multiplexing of this type of fiber-optic refractive index sensors is investigated. It is estimated that multiplexing of more than 10 such sensors is possible. In the multiplexing scheme, one of the sensors is used to investigate the refractive index and temperature responses. The fast Fourier transform (FFT) of the combined reflective spectra is analyzed. The intensity of the FFT spectra is linearly related with the refractive index and is not sensitive to the temperature.
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This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic

This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.
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Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this

Autonomous driving is a challenging problem, particularly when the domain is unstructured, as in an outdoor agricultural setting. Thus, advanced perception systems are primarily required to sense and understand the surrounding environment recognizing artificial and natural structures, topology, vegetation and paths. In this paper, a self-learning framework is proposed to automatically train a ground classifier for scene interpretation and autonomous navigation based on multi-baseline stereovision. The use of rich 3D data is emphasized where the sensor output includes range and color information of the surrounding environment. Two distinct classifiers are presented, one based on geometric data that can detect the broad class of ground and one based on color data that can further segment ground into subclasses. The geometry-based classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the system automatically learns to associate geometric appearance of 3D stereo-generated data with class labels. Then, it makes predictions based on past observations. It serves as well to provide training labels to the color-based classifier. Once trained, the color-based classifier is able to recognize similar terrain classes in stereo imagery. The system is continuously updated online using the latest stereo readings, thus making it feasible for long range and long duration navigation, over changing environments. Experimental results, obtained with a tractor test platform operating in a rural environment, are presented to validate this approach, showing an average classification precision and recall of 91.0% and 77.3%, respectively.
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To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this

To overcome the performance degradation in the presence of steering vector mismatches, strict restrictions on the number of available snapshots, and numerous interferences, a novel beamforming approach based on nonlinear least-square support vector regression machine (LS-SVR) is derived in this paper. In this approach, the conventional linearly constrained minimum variance cost function used by minimum variance distortionless response (MVDR) beamformer is replaced by a squared-loss function to increase robustness in complex scenarios and provide additional control over the sidelobe level. Gaussian kernels are also used to obtain better generalization capacity. This novel approach has two highlights, one is a recursive regression procedure to estimate the weight vectors on real-time, the other is a sparse model with novelty criterion to reduce the final size of the beamformer. The analysis and simulation tests show that the proposed approach offers better noise suppression capability and achieve near optimal signal-to-interference-and-noise ratio (SINR) with a low computational burden, as compared to other recently proposed robust beamforming techniques.
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In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model

In previous attempts to identify aquatic vegetation from remotely-sensed images using classification trees (CT), the images used to apply CT models to different times or locations necessarily originated from the same satellite sensor as that from which the original images used in model development came, greatly limiting the application of CT. We have developed an effective normalization method to improve the robustness of CT models when applied to images originating from different sensors and dates. A total of 965 ground-truth samples of aquatic vegetation types were obtained in 2009 and 2010 in Taihu Lake, China. Using relevant spectral indices (SI) as classifiers, we manually developed a stable CT model structure and then applied a standard CT algorithm to obtain quantitative (optimal) thresholds from 2009 ground-truth data and images from Landsat7-ETM+, HJ-1B-CCD, Landsat5-TM and ALOS-AVNIR-2 sensors. Optimal CT thresholds produced average classification accuracies of 78.1%, 84.7% and 74.0% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. However, the optimal CT thresholds for different sensor images differed from each other, with an average relative variation (RV) of 6.40%. We developed and evaluated three new approaches to normalizing the images. The best-performing method (Method of 0.1% index scaling) normalized the SI images using tailored percentages of extreme pixel values. Using the images normalized by Method of 0.1% index scaling, CT models for a particular sensor in which thresholds were replaced by those from the models developed for images originating from other sensors provided average classification accuracies of 76.0%, 82.8% and 68.9% for emergent vegetation, floating-leaf vegetation and submerged vegetation, respectively. Applying the CT models developed for normalized 2009 images to 2010 images resulted in high classification (78.0%–93.3%) and overall (92.0%–93.1%) accuracies. Our results suggest that Method of 0.1% index scaling provides a feasible way to apply CT models directly to images from sensors or time periods that differ from those of the images used to develop the original models.
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Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on

Inertial Navigation Systems (INS) consist of accelerometers, gyroscopes and a processor that generates position and orientation solutions by integrating the specific forces and rotation rates. In addition to the accelerometers and gyroscopes, magnetometers can be used to derive the user heading based on Earth’s magnetic field. Unfortunately, the measurements of the magnetic field obtained with low cost sensors are usually corrupted by several errors, including manufacturing defects and external electro-magnetic fields. Consequently, proper calibration of the magnetometer is required to achieve high accuracy heading measurements. In this paper, a Particle Swarm Optimization (PSO)-based calibration algorithm is presented to estimate the values of the bias and scale factor of low cost magnetometers. The main advantage of this technique is the use of the artificial intelligence which does not need any error modeling or awareness of the nonlinearity. Furthermore, the proposed algorithm can help in the development of Pedestrian Navigation Devices (PNDs) when combined with inertial sensors and GPS/Wi-Fi for indoor navigation and Location Based Services (LBS) applications.
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The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital

The technological advances in medical sensors, low-power microelectronics and miniaturization, wireless communications and networks have enabled the appearance of a new generation of wireless sensor networks: the so-called wireless body area networks (WBAN). These networks can be used for continuous monitoring of vital parameters, movement, and the surrounding environment. The data gathered by these networks contributes to improve users’ quality of life and allows the creation of a knowledge database by using learning techniques, useful to infer abnormal behaviour. In this paper we present a wireless body area network architecture to recognize human movement, identify human postures and detect harmful activities in order to prevent risk situations. The WBAN was created using tiny, cheap and low-power nodes with inertial and physiological sensors, strategically placed on the human body. Doing so, in an as ubiquitous as possible way, ensures that its impact on the users’ daily actions is minimum. The information collected by these sensors is transmitted to a central server capable of analysing and processing their data. The proposed system creates movement profiles based on the data sent by the WBAN’s nodes, and is able to detect in real time any abnormal movement and allows for a monitored rehabilitation of the user.
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Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate

Automatic classification of fruits via computer vision is still a complicated task due to the various properties of numerous types of fruits. We propose a novel classification method based on a multi-class kernel support vector machine (kSVM) with the desirable goal of accurate and fast classification of fruits. First, fruit images were acquired by a digital camera, and then the background of each image was removed by a split-and-merge algorithm; Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space; Third, principal component analysis (PCA) was used to reduce the dimensions of feature space; Finally, three kinds of multi-class SVMs were constructed, i.e., Winner-Takes-All SVM, Max-Wins-Voting SVM, and Directed Acyclic Graph SVM. Meanwhile, three kinds of kernels were chosen, i.e., linear kernel, Homogeneous Polynomial kernel, and Gaussian Radial Basis kernel; finally, the SVMs were trained using 5-fold stratified cross validation with the reduced feature vectors as input. The experimental results demonstrated that the Max-Wins-Voting SVM with Gaussian Radial Basis kernel achieves the best classification accuracy of 88.2%. For computation time, the Directed Acyclic Graph SVMs performs swiftest.
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Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable

Rapid and specific detection of avian influenza virus (AIV) is urgently needed due to the concerns over the potential outbreaks of highly pathogenic H5N1 influenza in animals and humans. Aptamers are artificial oligonucleic acids that can bind specific target molecules, and show comparable affinity for target viruses and better thermal stability than monoclonal antibodies. The objective of this research was to use a DNA-aptamer as the specific recognition element in a portable Surface Plasmon Resonance (SPR) biosensor for rapid detection of AIV H5N1 in poultry swab samples. A SPR biosensor was fabricated using selected aptamers that were biotinylated and then immobilized on the sensor gold surface coated with streptavidin via streptavidin-biotin binding. The immobilized aptamers captured AIV H5N1 in a sample solution, which caused an increase in the refraction index (RI). After optimizing the streptavidin and aptamer parameters, the results showed that the RI value was linearly related (R2 = 0.99) to the concentration of AIV in the range of 0.128 to 1.28 HAU. Negligible signal ( < 4% of H5N1) was observed from six non-target AIV subtypes. The AIV H5N1 in poultry swab samples with concentrations of 0.128 to 12.8 HAU could be detected using this aptasensor in 1.5 h.
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We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture

We present an empirical quality control protocol for above-water radiometric sampling focussing on identifying sunglint situations. Using hyperspectral radiometers, measurements were taken on an automated and unmanned seaborne platform in northwest European shelf seas. In parallel, a camera system was used to capture sea surface and sky images of the investigated points. The quality control consists of meteorological flags, to mask dusk, dawn, precipitation and low light conditions, utilizing incoming solar irradiance (ES) spectra. Using 629 from a total of 3,121 spectral measurements that passed the test conditions of the meteorological flagging, a new sunglint flag was developed. To predict sunglint conspicuous in the simultaneously available sea surface images a sunglint image detection algorithm was developed and implemented. Applying this algorithm, two sets of data, one with (having too much or detectable white pixels or sunglint) and one without sunglint (having least visible/detectable white pixel or sunglint), were derived. To identify the most effective sunglint flagging criteria we evaluated the spectral characteristics of these two data sets using water leaving radiance (LW) and remote sensing reflectance (RRS). Spectral conditions satisfying ‘mean LW(700–950 nm) < 2 mW∙m−2∙nm−1∙Sr−1’ or alternatively ‘minimum RRS (700–950 nm) < 0.010 Sr−1’, mask most measurements affected by sunglint, providing an efficient empirical flagging of sunglint in automated quality control.
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Species discrimination among three kinds of puffer fish, Takifuguobscurus, Takifuguflavidus and Takifugurubripes, was conducted using an electronic nose combined with olfactory sensory evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA) and

Species discrimination among three kinds of puffer fish, Takifuguobscurus, Takifuguflavidus and Takifugurubripes, was conducted using an electronic nose combined with olfactory sensory evaluation. All data were treated by multivariate data processing based on principal component analysis (PCA) and discriminant factor analysis (DFA). The results showed the discriminant model by PCA method and DFA method. Using PCA and DFA, it was shown that the electronic nose was able to reasonably distinguish between each of the eleven puffer fish groups, with a discrimination index of 85. The olfactory sensory evaluation was undertaken in accordance to Sensory analysis—Methodology—Initiation and training of assessors in the detection and recognition of odors (BS ISO 5496-2006), and the results showed that the evaluation was able to identify puffer fish samples according to their species, geographical origin and age. Results from this analysis demonstrate that the E-nose can be used to complement the discrimination of odors by sensory evaluation from the three species of puffer fish studied here.
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This paper demonstrates a modular architecture of a non-contact actuation micropump setup. Rapid hot embossing prototyping was employed in micropump fabrication by using printed circuit board (PCB) as a mold material in polymer casting. Actuator-membrane gap separation was studied, with experimental investigation of

This paper demonstrates a modular architecture of a non-contact actuation micropump setup. Rapid hot embossing prototyping was employed in micropump fabrication by using printed circuit board (PCB) as a mold material in polymer casting. Actuator-membrane gap separation was studied, with experimental investigation of three separation distances: 2.0 mm, 2.5 mm and 3.5 mm. To enhance the micropump performance, interaction surface area between plunger and membrane was modeled via finite element analysis (FEA). The micropump was evaluated against two frequency ranges, which comprised a low driving frequency range (0–5 Hz, with 0.5 Hz step increments) and a nominal frequency range (0–80 Hz, with 10 Hz per step increments). The low range frequency features a linear relationship of flow rate with the operating frequency function, while two magnitude peaks were captured in the flow rate and back pressure characteristic in the nominal frequency range. Repeatability and reliability tests conducted suggest the pump performed at a maximum flow rate of 5.78 mL/min at 65 Hz and a backpressure of 1.35 kPa at 60 Hz.
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Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware

Recent developments in smartphones have increased the processing capabilities and equipped these devices with a number of built-in multimodal sensors, including accelerometers, gyroscopes, GPS interfaces, Wi-Fi access, and proximity sensors. Despite the fact that numerous studies have investigated the development of user-context aware applications using smartphones, these applications are currently only able to recognize simple contexts using a single type of sensor. Therefore, in this work, we introduce a comprehensive approach for context aware applications that utilizes the multimodal sensors in smartphones. The proposed system is not only able to recognize different kinds of contexts with high accuracy, but it is also able to optimize the power consumption since power-hungry sensors can be activated or deactivated at appropriate times. Additionally, the system is able to recognize activities wherever the smartphone is on a human’s body, even when the user is using the phone to make a phone call, manipulate applications, play games, or listen to music. Furthermore, we also present a novel feature selection algorithm for the accelerometer classification module. The proposed feature selection algorithm helps select good features and eliminates bad features, thereby improving the overall accuracy of the accelerometer classifier. Experimental results show that the proposed system can classify eight activities with an accuracy of 92.43%.
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Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be

Recent technological advances in wireless communications and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices, which can be integrated into a Wireless Body Sensor Network (WBSN) for health monitoring. Physiological signals of humans such as heartbeats, temperature and pulse can be monitored from a distant location using tiny biomedical wireless sensors. Hence, it is highly essential to combine the ubiquitous computing with mobile health technology using wireless sensors and smart phones to monitor the well-being of chronic patients such as cardiac, Parkinson and epilepsy patients. Since physiological data of a patient are highly sensitive, maintaining its confidentiality is highly essential. Hence, security is a vital research issue in mobile health (mHealth) applications, especially if a patient has an embarrassing disease. In this paper a three tier security architecture for the mHealth application is proposed, in which light weight data confidentiality and authentication protocols are proposed to maintain the privacy of a patient. Moreover, considering the energy and hardware constraints of the wireless body sensors, low complexity data confidential and authentication schemes are designed. Performance evaluation of the proposed architecture shows that they can satisfy the energy and hardware limitations of the sensors and still can maintain the secure fabrics of the wireless body sensor networks. Besides, the proposed schemes can outperform in terms of energy consumption, memory usage and computation time over standard key establishment security scheme.
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Network lifetime is a crucial concern for Wireless Multimedia Sensor Networks (WMSNs), particularly due to the energy constraints of their nodes and the significant bitrate required by multimedia applications. This paper deals with this issue, studying how to achieve the maximum network lifetime,

Network lifetime is a crucial concern for Wireless Multimedia Sensor Networks (WMSNs), particularly due to the energy constraints of their nodes and the significant bitrate required by multimedia applications. This paper deals with this issue, studying how to achieve the maximum network lifetime, and simultaneously satisfying the best aggregate throughput for the multimedia services offered. To this end, we propose a planning model that results in a more accurate solution for an arbitrary network deployment than using the current optimization techniques applied both to WMSNs and traditional Wireless Sensor Networks (WSNs). Our model is based on multi-objective formulation and goal programming, which, to the best of our knowledge, have never been employed in the WSN field. This paper also proposes a load balancing algorithm which ensures a fair traffic load distribution per link during the network operation and matches the values returned by the mathematical planning model for the set lifetime and throughput. Simulation results are presented and further discussed to show the effectiveness of the numerical solutions. Finally, a test-bed deployed in a trial environment validates the theoretical contributions of this work.
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This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both

This paper presents a novel feature point descriptor for the multispectral image case: Far-Infrared and Visible Spectrum images. It allows matching interest points on images of the same scene but acquired in different spectral bands. Initially, points of interest are detected on both images through a SIFT-like based scale space representation. Then, these points are characterized using an Edge Oriented Histogram (EOH) descriptor. Finally, points of interest from multispectral images are matched by finding nearest couples using the information from the descriptor. The provided experimental results and comparisons with similar methods show both the validity of the proposed approach as well as the improvements it offers with respect to the current state-of-the-art.
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This paper describes the underlying methodology behind an adaptive multimodal radar sensor that is capable of progressively optimizing its range resolution depending upon the target scattering features. It consists of a test-bed that enables the generation of linear frequency modulated waveforms of various

This paper describes the underlying methodology behind an adaptive multimodal radar sensor that is capable of progressively optimizing its range resolution depending upon the target scattering features. It consists of a test-bed that enables the generation of linear frequency modulated waveforms of various bandwidths. This paper discusses a theoretical approach to optimizing the bandwidth used by the multimodal radar. It also discusses the various experimental results obtained from measurement. The resolution predicted from theory agrees quite well with that obtained from experiments for different target arrangements.
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Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In

Optical flow algorithms offer a way to estimate motion from a sequence of images. The computation of optical flow plays a key-role in several computer vision applications, including motion detection and segmentation, frame interpolation, three-dimensional scene reconstruction, robot navigation and video compression. In the case of gradient based optical flow implementation, the pre-filtering step plays a vital role, not only for accurate computation of optical flow, but also for the improvement of performance. Generally, in optical flow computation, filtering is used at the initial level on original input images and afterwards, the images are resized. In this paper, we propose an image filtering approach as a pre-processing step for the Lucas-Kanade pyramidal optical flow algorithm. Based on a study of different types of filtering methods and applied on the Iterative Refined Lucas-Kanade, we have concluded on the best filtering practice. As the Gaussian smoothing filter was selected, an empirical approach for the Gaussian variance estimation was introduced. Tested on the Middlebury image sequences, a correlation between the image intensity value and the standard deviation value of the Gaussian function was established. Finally, we have found that our selection method offers a better performance for the Lucas-Kanade optical flow algorithm.
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A non-dispersive infrared (NDIR) methane gas sensor prototype has achieved a minimum detection limit of 1 parts per million by volume (ppm). The central idea of the design of the sensor is to decrease the detection limit by increasing the signal to noise

A non-dispersive infrared (NDIR) methane gas sensor prototype has achieved a minimum detection limit of 1 parts per million by volume (ppm). The central idea of the design of the sensor is to decrease the detection limit by increasing the signal to noise ratio (SNR) of the system. In order to decrease the noise level, a single frequency filter algorithm based on fast Fourier transform (FFT) is adopted for signal processing. Through simulation and experiment, it is found that the full width at half maximum (FWHM) of the filter narrows with the extension of sampling period and the increase of lamp modulation frequency, and at some optimum sampling period and modulation frequency, the filtered signal maintains a noise to signal ratio of below 1/10,000. The sensor prototype provides the key techniques for a hand-held methane detector that has a low cost and a high resolution. Such a detector may facilitate the detection of leakage of city natural gas pipelines buried underground, the monitoring of landfill gas, the monitoring of air quality and so on.
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In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data

In this paper, we consider the problem of autonomous exploration of unknown environments with single and multiple robots. This is a challenging task, with several potential applications. We propose a simple yet effective approach that combines a behavior-based navigation with an efficient data structure to store previously visited regions. This allows robots to safely navigate, disperse and efficiently explore the environment. A series of experiments performed using a realistic robotic simulator and a real testbed scenario demonstrate that our technique effectively distributes the robots over the environment and allows them to quickly accomplish their mission in large open spaces, narrow cluttered environments, dead-end corridors, as well as rooms with minimum exits.
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This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can

This study explores the feasibility of applying single-scan airborne, static terrestrial and mobile laser scanning for improving the accuracy of tree height growth measurement. Specifically, compared to the traditional works on forest growth inventory with airborne laser scanning, two issues are regarded: “Can the new technique characterize the height growth for each individual tree?” and “Can this technique refine the minimum growth-discernable temporal interval further?” To solve these two puzzles, the sampling principles of the three laser scanning modes were first examined, and their error sources against the task of tree-top capturing were also analyzed. Next, the three-year growths of 58 Nordic maple trees (Crimson King) for test were intermittently surveyed with one type of laser scanning each time and then analyzed by statistics. The evaluations show that the height growth of each individual tree still cannot be reliably characterized even by single-scan terrestrial laser scanning, and statistical analysis is necessary in this scenario. After Gaussian regression, it is found that the minimum temporal interval with distinguishable tree height growths can be refined into one month based on terrestrial laser scanning, far better than the two years deduced in the previous works based on airborne laser scanning. The associated mean growth was detected to be about 0.12 m. Moreover, the parameter of tree height generally under-estimated by airborne and even mobile laser scanning can be relatively revised by means of introducing static terrestrial laser scanning data. Overall, the effectiveness of the proposed technique is primarily validated.
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The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser

The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality.
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A restoration method for reflection spectra in a serial FBG sensor array with spectral shadowing is proposed and experimentally demonstrated in a WDM/TDM combined multiplexing system. The SNR of each FBG sensor is formulated and analyzed as a function of the number and

A restoration method for reflection spectra in a serial FBG sensor array with spectral shadowing is proposed and experimentally demonstrated in a WDM/TDM combined multiplexing system. The SNR of each FBG sensor is formulated and analyzed as a function of the number and reflectivities of serial FBG sensors. The maximum number of FBG sensors in a single fiber line can be determined by the approximate formula. In the test using two FBG sensors, the restored reflection spectrum of second FBG sensor is shown to be very well matched with the original reflection spectrum. Using the proposed restoration method, the maximum peak detection error in a strain experiment is suppressed drastically by almost seven-fold, from 0.074 nm to 0.011 nm.
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Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification

Long term continuous monitoring of electrocardiogram (ECG) in a free living environment provides valuable information for prevention on the heart attack and other high risk diseases. This paper presents the design of a real-time wearable ECG monitoring system with associated cardiac arrhythmia classification algorithms. One of the striking advantages is that ECG analog front-end and on-node digital processing are designed to remove most of the noise and bias. In addition, the wearable sensor node is able to monitor the patient’s ECG and motion signal in an unobstructive way. To realize the real-time medical analysis, the ECG is digitalized and transmitted to a smart phone via Bluetooth. On the smart phone, the ECG waveform is visualized and a novel layered hidden Markov model is seamlessly integrated to classify multiple cardiac arrhythmias in real time. Experimental results demonstrate that the clean and reliable ECG waveform can be captured in multiple stressed conditions and the real-time classification on cardiac arrhythmia is competent to other workbenches.
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Paper-based sensors are a new alternative technology for fabricating simple, low-cost, portable and disposable analytical devices for many application areas including clinical diagnosis, food quality control and environmental monitoring. The unique properties of paper which allow passive liquid transport and compatibility with chemicals/biochemicals

Paper-based sensors are a new alternative technology for fabricating simple, low-cost, portable and disposable analytical devices for many application areas including clinical diagnosis, food quality control and environmental monitoring. The unique properties of paper which allow passive liquid transport and compatibility with chemicals/biochemicals are the main advantages of using paper as a sensing platform. Depending on the main goal to be achieved in paper-based sensors, the fabrication methods and the analysis techniques can be tuned to fulfill the needs of the end-user. Current paper-based sensors are focused on microfluidic delivery of solution to the detection site whereas more advanced designs involve complex 3-D geometries based on the same microfluidic principles. Although paper-based sensors are very promising, they still suffer from certain limitations such as accuracy and sensitivity. However, it is anticipated that in the future, with advances in fabrication and analytical techniques, that there will be more new and innovative developments in paper-based sensors. These sensors could better meet the current objectives of a viable low-cost and portable device in addition to offering high sensitivity and selectivity, and multiple analyte discrimination. This paper is a review of recent advances in paper-based sensors and covers the following topics: existing fabrication techniques, analytical methods and application areas. Finally, the present challenges and future outlooks are discussed.
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The use of a functional molecular unit acting as a state variable provides an attractive alternative for the next generations of nanoscale electronics. It may help overcome the limits of conventional MOSFETd due to their potential scalability, low-cost, low variability, and highly integratable

The use of a functional molecular unit acting as a state variable provides an attractive alternative for the next generations of nanoscale electronics. It may help overcome the limits of conventional MOSFETd due to their potential scalability, low-cost, low variability, and highly integratable characteristics as well as the capability to exploit bottom-up self-assembly processes. This bottom-up construction and the operation of nanoscale machines/devices, in which the molecular motion can be controlled to perform functions, have been studied for their functionalities. Being triggered by external stimuli such as light, electricity or chemical reagents, these devices have shown various functions including those of diodes, rectifiers, memories, resonant tunnel junctions and single settable molecular switches that can be electronically configured for logic gates. Molecule-specific electronic switching has also been reported for several of these device structures, including nanopores containing oligo(phenylene ethynylene) monolayers, and planar junctions incorporating rotaxane and catenane monolayers for the construction and operation of complex molecular machines. A specific electrically driven surface mounted molecular rotor is described in detail in this review. The rotor is comprised of a monolayer of redox-active ligated copper compounds sandwiched between a gold electrode and a highly-doped P+ Si. This electrically driven sandwich-type monolayer molecular rotor device showed an on/off ratio of approximately 104, a read window of about 2.5 V, and a retention time of greater than 104 s. The rotation speed of this type of molecular rotor has been reported to be in the picosecond timescale, which provides a potential of high switching speed applications. Current-voltage spectroscopy (I-V) revealed a temperature-dependent negative differential resistance (NDR) associated with the device. The analysis of the device I–V characteristics suggests the source of the observed switching effects to be the result of the redox-induced ligand rotation around the copper metal center and this attribution of switching is consistent with the observed temperature dependence of the switching behavior as well as the proposed energy diagram of the device. The observed resistance switching shows the potential for future non-volatile memories and logic devices applications. This review will discuss the progress and provide a perspective of molecular motion for nanoelectronics and other applications.
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Mobile laser scanning is an emerging technology capable of capturing three-dimensional data from surrounding objects. With state-of-the-art sensors, the achieved point clouds capture object details with good accuracy and precision. Many of the applications involve civil engineering in urban areas, as well as

Mobile laser scanning is an emerging technology capable of capturing three-dimensional data from surrounding objects. With state-of-the-art sensors, the achieved point clouds capture object details with good accuracy and precision. Many of the applications involve civil engineering in urban areas, as well as traffic and other urban planning, all of which serve to make 3D city modeling probably the fastest growing market segment in this field. This article outlines multiplatform mobile laser scanning solutions such as vehicle- and trolley-operated urban area data acquisition, and boat-mounted equipment for fluvial environments. Moreover, we introduce a novel backpack version of mobile laser scanning equipment for surveying applications in the field of natural sciences where the requirements include precision and mobility in variable terrain conditions. In addition to presenting a technical description of the systems, we discuss the performance of the solutions in the light of various applications in the fields of urban mapping and modeling, fluvial geomorphology, snow-cover characterization, precision agriculture, and in monitoring the effects of climate change on permafrost landforms. The data performance of the mobile laser scanning approach is described by the results of an evaluation of the ROAMER on a permanent MLS test field. Furthermore, an insitu accuracy assessment using a field of spherical 3D targets for the newly-introduced Akhka backpack system is conducted and reported on.
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Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic

Chlorophyll fluorescence can be defined as the red and far-red light emitted by photosynthetic tissue when it is excited by a light source. This is an important phenomenon which permits investigators to obtain important information about the state of health of a photosynthetic sample. This article reviews the current state of the art knowledge regarding the design of new chlorophyll fluorescence sensing systems, providing appropriate information about processes, instrumentation and electronic devices. These types of systems and applications can be created to determine both comfort conditions and current problems within a given subject. The procedure to measure chlorophyll fluorescence is commonly split into two main parts; the first involves chlorophyll excitation, for which there are passive or active methods. The second part of the procedure is to closely measure the chlorophyll fluorescence response with specialized instrumentation systems. Such systems utilize several methods, each with different characteristics regarding to cost, resolution, ease of processing or portability. These methods for the most part include cameras, photodiodes and satellite images.
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The recent advances of polymer technology allowed the introduction of plastic optical fiber in sensor design. The advantages of optical metrology with plastic optical fiber have attracted the attention of the scientific community, as they allow the development of low-cost or cost competitive

The recent advances of polymer technology allowed the introduction of plastic optical fiber in sensor design. The advantages of optical metrology with plastic optical fiber have attracted the attention of the scientific community, as they allow the development of low-cost or cost competitive systems compared with conventional technologies. In this paper, the current state of the art of plastic optical fiber technology will be reviewed, namely its main characteristics and sensing advantages. Several measurement techniques will be described, with a strong focus on interrogation approaches based on intensity variation in transmission and reflection. The potential applications involving structural health monitoring, medicine, environment and the biological and chemical area are also presented.
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In this manuscript, we present a survey of designs and implementations of research sensor nodes that rely on FPGAs, either based upon standalone platforms or as a combination of microcontroller and FPGA. Several current challenges in sensor networks are distinguished and linked to

In this manuscript, we present a survey of designs and implementations of research sensor nodes that rely on FPGAs, either based upon standalone platforms or as a combination of microcontroller and FPGA. Several current challenges in sensor networks are distinguished and linked to the features of modern FPGAs. As it turns out, low-power optimized FPGAs are able to enhance the computation of several types of algorithms in terms of speed and power consumption in comparison to microcontrollers of commercial sensor nodes. We show that architectures based on the combination of microcontrollers and FPGA can play a key role in the future of sensor networks, in fields where processing capabilities such as strong cryptography, self-testing and data compression, among others, are paramount.
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Accurate prediction of the adverse effects of test compounds on living systems, detection of toxic thresholds, and expansion of experimental data sets to include multiple toxicity end-point analysis are required for any robust screening regime. Alamar Blue is an important redox indicator that

Accurate prediction of the adverse effects of test compounds on living systems, detection of toxic thresholds, and expansion of experimental data sets to include multiple toxicity end-point analysis are required for any robust screening regime. Alamar Blue is an important redox indicator that is used to evaluate metabolic function and cellular health. The Alamar Blue bioassay has been utilized over the past 50 years to assess cell viability and cytotoxicity in a range of biological and environmental systems and in a number of cell types including bacteria, yeast, fungi, protozoa and cultured mammalian and piscine cells. It offers several advantages over other metabolic indicators and other cytotoxicity assays. However, as with any bioassay, suitability must be determined for each application and cell model. This review seeks to highlight many of the important considerations involved in assay use and design in addition to the potential pitfalls.
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This work presents a brief introduction on the basics of fiber-optical sensors and an overview focused on the applications to measurements in multiphase reactors. The most commonly principle utilized is laser back scattering, which is also the foundation for almost all current probes

This work presents a brief introduction on the basics of fiber-optical sensors and an overview focused on the applications to measurements in multiphase reactors. The most commonly principle utilized is laser back scattering, which is also the foundation for almost all current probes used in multiphase reactors. The fiber-optical probe techniques in two-phase reactors are more developed than those in three-phase reactors. There are many studies on the measurement of gas holdup using fiber-optical probes in three-phase fluidized beds, but negative interference of particles on probe function was less studied. The interactions between solids and probe tips were less studied because glass beads etc. were always used as the solid phase. The vision probes may be the most promising for simultaneous measurements of gas dispersion and solids suspension in three-phase reactors. Thus, the following techniques of the fiber-optical probes in multiphase reactors should be developed further: (1) online measuring techniques under nearly industrial operating conditions; (2) corresponding signal data processing techniques; (3) joint application with other measuring techniques.
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Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and

Due to insufficient biomarker validation and poor performances in diagnostic assays, the candidate biomarker verification process has to be improved. Multi-analyte immunoassays are the tool of choice for the identification and detailed validation of protein biomarkers in serum. The process of identification and validation of serum biomarkers, as well as their implementation in diagnostic routine requires an application of independent immunoassay platforms with the possibility of high-throughput. This review will focus on three main multi-analyte immunoassay platforms: planar microarrays, multiplex bead systems and, array-based surface plasmon resonance (SPR) chips. Recent developments of each platform will be discussed for application in clinical proteomics, principles, detection methods, and performance strength. The requirements for specific surface functionalization of assay platforms are continuously increasing. The reasons for this increase is the demand for highly sensitive assays, as well as the reduction of non-specific adsorption from complex samples, and with it high signal-to-noise-ratios. To achieve this, different support materials were adapted to the immobilized biomarker/ligand, allowing a high binding capacity and immobilization efficiency. In the case of immunoassays, the immobilized ligands are proteins, antibodies or peptides, which exhibit a diversity of chemical properties (acidic/alkaline; hydrophobic/hydrophilic; secondary or tertiary structure/linear). Consequently it is more challenging to develop immobilization strategies necessary to ensure a homogenous covered surface and reliable assay in comparison to DNA immobilization. New developments concerning material support for each platform are discussed especially with regard to increase the immobilization efficiency and reducing the non-specific adsorption from complex samples like serum and cell lysates.
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Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water

Ecotoxicology faces the challenge of assessing and predicting the effects of an increasing number of chemical stressors on aquatic species and ecosystems. Herein we review currently applied tools in ecological risk assessment, combining information on exposure with expected biological effects or environmental water quality standards; currently applied effect-based tools are presented based on whether exposure occurs in a controlled laboratory environment or in the field. With increasing ecological relevance the reproducibility, specificity and thus suitability for standardisation of methods tends to diminish. We discuss the use of biomarkers in ecotoxicology including ecotoxicogenomics-based endpoints, which are becoming increasingly important for the detection of sublethal effects. Carefully selected sets of biomarkers allow an assessment of exposure to and effects of toxic chemicals, as well as the health status of organisms and, when combined with chemical analysis, identification of toxicant(s). The promising concept of “adverse outcome pathways (AOP)” links mechanistic responses on the cellular level with whole organism, population, community and potentially ecosystem effects and services. For most toxic mechanisms, however, practical application of AOPs will require more information and the identification of key links between responses, as well as key indicators, at different levels of biological organization, ecosystem functioning and ecosystem services.
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There are two mistakes in this article [1]. On page 523, lines 10–11, the sentence “the value of the SAM is 1, but the value of the ESAM is less than 1” should be “the value of the SAM is 0, but the value of the ESAM is great than 0”. Line 12, “but the value of the ESAM is less than 1” should be “but the value of the ESAM is great than 0 for even n”. [...]
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